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Findings from previous studies suggested that the cluster of the differentiation 8 alpha (CD8A) gene plays a prominent role in human T lymphocyte subpopulations. However, the evidence from pig population is still rare. To determine whether the important role of the CD8A gene is conserved in pig, a candidate gene analysis was performed herein through genotype-phenotype associations. Five single-nucleotide polymorphisms (SNPs) locating in the regulatory region of porcine CD8A gene were detected and tested for association analysis with seven T lymphocyte subpopulations (proportion of CD4(-)CD8(-), CD4(+)CD8(+), CD4(+)CD8(-), CD4(-)CD8(+), CD4(+), CD8(+), and the ratio of CD4(+) to CD8(+) T cells in peripheral blood) in 382 Large White piglets. After Bonferroni correction for multiple testing, four SNPs were significantly associated with some or all of the seven T lymphocyte subpopulations. Analyses of pairwise D' measures of linkage disequilibrium between all SNPs were also explored. Two haplotype blocks was inferred and the association study on haplotype level revealed similar effects on T lymphocyte subpopulations. In addition, the tissue-specific RNA expression pattern and electrophoretic mobility shift assay offered further explanation of the link between the CD8A gene with porcine T lymphocyte subpopulations. The findings presented here provide strong evidence for associations of CD8A variants with T lymphocyte subpopulations and may be applied in porcine breeding programs.

The bacterial strain enterotoxigenic Escherichia coli (ETEC) expressing F4 fimbriae is the major pathogen causing potentially fatal diarrhoea in neonatal and recently-weaned piglets. Our previously published results from a genome wide association study (GWAS) of the pig genome identified a promising candidate gene (ITGB5) for predicting the susceptibility to ETEC F4ab/ac infection, the gene encoding the integrin beta 5 (ITGB5) receptor. We report here the assembly and cloning of the complete porcine ITGB5 gene based on the latest reference sequence for swine, as well as our results following analysis of the coding regions of ITGB5 from a sample of pigs that included both resistant and susceptible animals. We identified a novel 12 bp deletion in the 5′ UTR region of the gene that correlates perfectly with increased ETEC F4ac adhesion in pig gut epithelial cells, in sample of 335 pigs from 3 different breeds. These results indicate that the ITGB5 gene is an important adhesion molecule for E. coli F4ac, and that the 12 bp deletion could serve as a genetic marker for selecting against susceptible pigs in breeding programs.

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

Imputation of missing genotypes, in particular from low density to high density, is an important issue in genomic selection and genome-wide association studies. Given the marker densities, the most important factors affecting imputation accuracy are the size of the reference population and the relationship between individuals in the reference (genotyped with high-density panel) and study (genotyped with low-density panel) populations. In this study, we investigated the imputation accuracies when the reference population (genotyped with Illumina BovineSNP50 SNP panel) contained sires, halfsibs, or both sires and halfsibs of the individuals in the study population (genotyped with Illumina BovineLD SNP panel) using three imputation programs (fimpute v2.2, findhap v2, and beagle v3.3.2). Two criteria, correlation between true and imputed genotypes and missing rate after imputation, were used to evaluate the performance of the three programs in different scenarios. Our results showed that fimpute performed the best in all cases, with correlations from 0.921 to 0.978 when imputing from sires to their daughters or between halfsibs. In general, the accuracies of imputing between halfsibs or from sires to their daughters were higher than were those imputing between non-halfsibs or from sires to non-daughters. Including both sires and halfsibs in the reference population did not improve the imputation performance in comparison with when only including halfsibs in the reference population for all the three programs.

With the Illumina BovineSNP50K BeadChip, we performed a genome-wide association study (GWAS) for two pigmentation traits in a Chinese Holstein population: proportion of black (PB) and teat colour (TC). A case-control design was used. Cases were the cows with PB0.30 (n = 129) and TC2 points (n = 140); controls were those with PB0.90 (n = 58) and TC4 points (n = 281). The RM test of roadtrips (version 1.2) was applied to detect SNPs for the two traits with 42 883 and 42 741 SNPs respectively. A total of nine and 12 genome-wide significant (P 0.05) SNPs associated with PB and TC respectively were identified. Of these, two SNPs for PB were located within the KIT and IGFBP7 genes, and the other four SNPs were 23~212 kb away from the PDGFRA gene on BTA6; nine SNPs associated with TC were located within or 21~78.8 kb away from known genes on chromosomes 4, 11, 22, 23 and 24. By combing through our GWAS results and the biological functions of the genes, we suggest that the KIT, IGFBP7, PDGFRA, MITF, ING3 and WNT16 genes are promising candidates for PB and TC in Holstein cattle, providing a basis for further investigation on the genetic mechanism of pigmentation formation.

Imputation of high-density genotypes from low- or medium-density platforms is a promising way to enhance the efficiency of whole-genome selection programs at low cost. In this study, we compared the efficiency of three widely used imputation algorithms (fastPHASE, BEAGLE and findhap) using Chinese Holstein cattle with Illumina BovineSNP50 genotypes. A total of 2108 cattle were randomly divided into a reference population and a test population to evaluate the influence of the reference population size. Three bovine chromosomes, BTA1, 16 and 28, were used to represent large, medium and small chromosome size, respectively. We simulated different scenarios by randomly masking 20%, 40%, 80% and 95% single-nucleotide polymorphisms (SNPs) on each chromosome in the test population to mimic different SNP density panels. Illumina Bovine3K and Illumina BovineLD (6909 SNPs) information was also used. We found that the three methods showed comparable accuracy when the proportion of masked SNPs was low. However, the difference became larger when more SNPs were masked. BEAGLE performed the best and was most robust with imputation accuracies >90% in almost all situations. fastPHASE was affected by the proportion of masked SNPs, especially when the masked SNP rate was high. findhap ran the fastest, whereas its accuracies were lower than those of BEAGLE but higher than those of fastPHASE. In addition, enlarging the reference population improved the imputation accuracy for BEAGLE and findhap, but did not affect fastPHASE. Considering imputation accuracy and computational requirements, BEAGLE has been found to be more reliable for imputing genotypes from low- to high-density genotyping platforms.

Cluster of differentiation 4 (CD4) is mainly expressed on CD4(+) T cells, which plays an important role in immune response. The aim of this study was to detect the association between polymorphisms of the CD4 gene and T lymphocyte subpopulations in pigs, and to investigate the effects of genetic variation on the CD4 gene expression level in immune tissues. Five missense mutations in the CD4 gene were identified using DNA pooling sequencing assays, and two main haplotypes (CCTCC and AGCTG) in strong linkage disequilibrium (with frequencies of 50.26% and 46.34%, respectively) were detected in the population of Large White pigs. Our results indicated that the five SNPs and the two haplotypes were significantly associated with the proportions of CD4(-)CD8(-), CD4(+)CD8(+), CD4(+)CD8(-), CD4(+) and CD4(+)/CD8(+) in peripheral blood (p0.05). Gene expression analysis showed the mRNA level of the CD4 gene in thymus was significantly higher than that in lymph node and spleen (p0.05). However, no significant difference was observed between animals with CCTCC/CCTCC genotype and animals with AGCTG/AGCTG genotype in the three immune tissues (p0.05). These results indicate that the CD4 gene may influence T lymphocyte subpopulations and can be considered as a candidate gene affecting immunity in pigs.

Background Identification of the genomic signatures of recent selection may help uncover causal polymorphisms controlling traits relevant to recent decades of selective breeding in livestock. In this study, we aimed at detecting signatures of recent selection in commercial broiler chickens using genotype information from single nucleotide polymorphisms (SNPs). A total of 565 chickens from five commercial purebred lines, including three broiler sire (male) lines and two broiler dam (female) lines, were genotyped using the 60K SNP Illumina iSelect chicken array. To detect genomic signatures of recent selection, we applied two methods based on population comparison, cross-population extended haplotype homozygosity (XP-EHH) and cross-population composite likelihood ratio (XP-CLR), and further analyzed the results to find genomic regions under recent selection in multiple purebred lines. Results A total of 321 candidate selection regions spanning approximately 1.45 % of the chicken genome in each line were detected by consensus of results of both XP-EHH and XP-CLR methods. To minimize false discovery due to genetic drift, only 42 of the candidate selection regions that were shared by 2 or more purebred lines were considered as high-confidence selection regions in the study. Of these 42 regions, 20 were 50 kb or less while 4 regions were larger than 0.5 Mb. In total, 91 genes could be found in the 42 regions, among which 19 regions contained only 1 or 2 genes, and 9 regions were located at gene deserts. Conclusions Our results provide a genome-wide scan of recent selection signatures in five purebred lines of commercial broiler chickens. We found several candidate genes for recent selection in multiple lines, such as SOX6 (Sex Determining Region Y-Box 6) and cTR (Thyroid hormone receptor beta). These genes may have been under recent selection due to their essential roles in growth, development and reproduction in chickens. Furthermore, our results suggest that in some candidate regions, the same or opposite alleles have been under recent selection in multiple lines. Most of the candidate genes in the selection regions are novel, and as such they should be of great interest for future research into the genetic architecture of traits relevant to modern broiler breeding. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0430-1) contains supplementary material, which is available to authorized users.

Methods and results of detecting selection signatures using ZH scores. (DOCX 32 kb)

Interferon regulatory factor 2 (IRF2) gene is a member of IRF-family and shown to play functionally diverse roles in the regulation of the immune system. In this report, the porcine IRF2 cDNA was cloned and its 14,000 bp genomic DNA structure was also identified. The putative IRF2 protein included 352 amino acids. Alignment analysis of the predicted porcine IRF2 amino acid sequences with their homologies of other species showed high identity (over 93%). Tissues expression of IRF2 mRNA was observed by RT-PCR, the results revealed IRF2 expressed widely in all analyzed tissues except skeletal muscle. Using the radiation hybrid panel, the porcine IRF2 gene was mapped to chromosome 15 and closely linked to microsatellite S0149 (LOD=6.17, 26 cR). A SNP ( GU295944 :c.1383 G>C) in exon 6 of porcine IRF2 gene was demonstrated by sequencing and PCR-RFLP analysis. The further association analysis indicated that the SNP was significantly associated with level of IL10 (at day 20), IFN-γ (at day 35) and ratio of IFN-γ/IL10 (at day 35) in serum ( P

Improving immune capacity may increase the profitability of animal production if it enables animals to better cope with infections. Hematological traits play pivotal roles in animal immune capacity and disease resistance. Thus far, few studies have been conducted using a high-density swine SNP chip panel to unravel the genetic mechanism of the immune capability in domestic animals. In this study, using mixed model-based single-locus regression analyses, we carried out genome-wide association studies, using the Porcine SNP60 BeadChip, for immune responses in piglets for 18 hematological traits (seven leukocyte traits, seven erythrocyte traits, and four platelet traits) after being immunized with classical swine fever vaccine. After adjusting for multiple testing based on permutations, 10, 24, and 77 chromosome-wise significant SNPs were identified for the leukocyte traits, erythrocyte traits, and platelet traits respectively, of which 10 reached genome-wise significance level. Among the 53 SNPs for mean platelet volume, 29 are located in a linkage disequilibrium block between 32.77 and 40.59 Mb on SSC6. Four genes of interest are located within the block, providing genetic evidence that this genomic segment may be considered a candidate region relevant to the platelet traits. Other candidate genes of interest for red blood cell, hemoglobin, and red blood cell volume distribution width also have been found near the significant SNPs. Our genome-wide association study provides a list of significant SNPs and candidate genes that offer valuable information for future dissection of molecular mechanisms regulating hematological traits.

Interleukin 10 ( IL10 ) gene is a pleiotropic cytokine that plays an essential role in cell-mediated immunity and humoral immunity. In this study, in order to better understand the effects of IL10 gene on serum IL10 concentration in pig, we firstly identified the genomic structure of porcine IL10 gene, revealed its mRNA tissue expression patterns, the polymorphisms of exons region of IL10 gene also were found, and then further association analysis of SNPs with serum IL10 level were carried out in 302 piglets from three pig populations. The 4,770 bp porcine IL10 genomic DNA contains a total of 175 amino acids in the putative IL10 protein. The alignment and phylogenetic analysis of porcine IL10 amino acid sequences with their homologies of other species displayed high identity. IL10 mRNA was widely expressed in nine analyzed tissues by RT-PCR. Totally three SNPs of IL10 gene were identified by directly sequencing of PCR fragments, two SNPs (HQ026020:g.474C>T; 484T>A) were in exon3 and the other one (HQ026020:g.3164G>A) was in exon5 region. Our statistic analysis results showed that the SNP (HQ026020:g.474C>T) had highly significant association with serum IL10 level at day 35 (P A) also was significantly associated with serum IL10 level at both day 20 and day 35(P IL10 gene in immune response and also indicated that IL10 gene may be used as a genetic marker with effects on serum IL10 level in the pig disease resistance breeding.

Interferon regulatory factor 6 (IRF6) gene is a member of the IRF-family, and plays functionally diverse roles in the regulation of the immune system. In this report, the 13,720 bp porcine IRF6 genomic DNA structure was firstly identified with a putative IRF6 protein of 467 amino acids. Alignment and phylogenetic analysis of the porcine IRF6 amino acid sequences with their homologies to other species showed high identity (over 96%). Tissues expression of IRF6 mRNA was observed by RT-PCR, the results revealed IRF6 expressed widely in eight tissues. One SNP ({"type":"entrez-nucleotide","attrs":{"text":"HQ026023","term_id":"317016901","term_text":"HQ026023"}}HQ026023:1383 G>C) in exon7 and two SNPs ({"type":"entrez-nucleotide","attrs":{"text":"HQ026023","term_id":"317016901","term_text":"HQ026023"}}HQ026023:130 G>A; 232 C>T) in the 5 ′ promoter region of porcine IRF6 gene were demonstrated b y DNA sequencing analysis. A further analysis of SNP genotypes associated with immune traits including IFN-γ and IL10 concentrations in serum was carried out in three pig populations including Large White, Landraces and Songliao Black pig (a Chinese indigenous breed). The results showed that the SNP ({"type":"entrez-nucleotide","attrs":{"text":"HQ026023","term_id":"317016901","term_text":"HQ026023"}}HQ026023:1383 G>C) was significantly associated with the level of IFN-γ (d 20) in serum (p = 0.038) and the ratio of IFN-γ to IL10 (d 20) in serum (p = 0.041); The other two SNPs ({"type":"entrez-nucleotide","attrs":{"text":"HQ026023","term_id":"317016901","term_text":"HQ026023"}}HQ026023:130 G>A; 232 C>T) were highly significantly associated with IL10 level in serum both at the day 20 (p = 0.005; p = 0.001) and the day 35 (p = 0.004; p = 0.006). Identification of the porcine IRF6 gene will help our further understanding of the molecular basis of the IFN regulation pathway in the porcine immune response. All these results should indicate that the IRF6 gene can be regarded as a molecular marker associated with the IL10 level in serum and used for genetic selection in the pig breeding.

Background Both genome-wide association (GWA) studies and genomic selection depend on the level of non-random association of alleles at different loci, i.e. linkage disequilibrium (LD), across the genome. Therefore, characterizing LD is of fundamental importance to implement both approaches. In this study, using a 60K single nucleotide polymorphism (SNP) panel, we estimated LD and haplotype structure in crossbred broiler chickens and their component pure lines (one male and two female lines) and calculated the consistency of LD between these populations. Results The average level of LD (measured by r2) between adjacent SNPs across the chicken autosomes studied here ranged from 0.34 to 0.40 in the pure lines but was only 0.24 in the crossbred populations, with 28.4% of adjacent SNP pairs having an r2 higher than 0.3. Compared with the pure lines, the crossbred populations consistently showed a lower level of LD, smaller haploblock sizes and lower haplotype homozygosity on macro-, intermediate and micro-chromosomes. Furthermore, correlations of LD between markers at short distances (0 to 10 kb) were high between crossbred and pure lines (0.83 to 0.94). Conclusions Our results suggest that using crossbred populations instead of pure lines can be advantageous for high-resolution QTL (quantitative trait loci) mapping in GWA studies and to achieve good persistence of accuracy of genomic breeding values over generations in genomic selection. These results also provide useful information for the design and implementation of GWA studies and genomic selection using crossbred populations. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0098-4) contains supplementary material, which is available to authorized users.

BACKGROUND: Integrin beta-5 (ITGB5) and mucin 13 (MUC13) genes are highly expressed on the apical surface of intestinal epithelia and are thought to be candidate genes for controlling the expression of the receptor for enterotoxigenic Escherichia coli (ETEC) F4ac. Human MUC13 protein has an expected role in protecting intestinal mucosal surfaces and porcine ITGB5 is a newly identified potential receptor for ETEC F4ac. METHODOLOGY/PRINCIPAL FINDINGS: To test the hypothesis that ITGB5 and MUC13 both play key roles in protection of the intestinal mucosa against pathogenic bacterium, porcine intestinal epithelial cells (IPEC-J2) were transfected with ITGB5-targeting, MUC13-targeting or negative control small interfering RNA (siRNA), respectively. Firstly, we measured mRNA expression levels of mucin genes (MUC4, MUC20), pro-inflammatory genes (IL8, IL1A, IL6, CXCL2), anti-inflammatory mediator SLPI, and PLAU after RNAi treatments with and without ETEC infection. Secondly, we compared the adhesions of ETEC to the pre- and post-knockdown IPEC-J2 cells of ITGB5 and MUC13, respectively. We found that ITGB5 and MUC13 knockdown both had small but significant effects in attenuating the inflammation induced by ETEC infection, and both increased bacterial adhesion in response to F4ac ETEC exposure. CONCLUSIONS/SIGNIFICANCE: Our current study first reported that ITGB5 and MUC13 are important adhesion molecules of mucosal epithelial signaling in response to Escherichia coli in pigs. These data suggest that both ITGB5 and MUC13 play key roles in defending the attachment and adhesion of ETEC to porcine jejunal cells and in maintaining epithelial barrier and immunity function.

Estimation of genomic breeding values is the key step in genomic selection (GS). Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of GS, and specifically, we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis of threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy with the genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (number of categories=2, incidence=30%, number of quantitative trait loci=50, h² = 0.3), the accuracies were improved by 30.4%, 2.4%, and 5.7% points, respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be the method of choice for GS of threshold traits.

Background Lymphocytes act as a major component of the adaptive immune system, taking very crucial responsibility for immunity. Differences in proportions of T-cell subpopulations in peripheral blood among individuals under same conditions provide evidence of genetic control on these traits, but little is known about the genetic mechanism of them, especially in swine. Identification of the genetic control on these variants may help the genetic improvement of immune capacity through selection. Results To identify genomic regions responsible for these immune traits in swine, a genome-wide association study was conducted. A total of 675 pigs of three breeds were involved in the study. At 21 days of age, all individuals were vaccinated with modified live classical swine fever vaccine. Blood samples were collected when the piglets were 20 and 35 days of age, respectively. Seven traits, including the proportions of CD4+, CD8+, CD4+CD8+, CD4+CD8−, CD4−CD8+, CD4−CD8− and the ratio of CD4+ to CD8+ T cells were measured at the two ages. All the samples were genotyped for 62,163 single nucleotide polymorphisms (SNP) using the Illumina porcineSNP60k BeadChip. 40833 SNPs were selected after quality control for association tests between SNPs and each immune trait considered based on a single-locus regression model. To tackle the issue of multiple testing in GWAS, 10,000 permutations were performed to determine the chromosome-wise and genome-wise significance levels of association tests. In total, 61 SNPs with chromosome-wise significance level and 3 SNPs with genome-wise significance level were identified. 27 significant SNPs were located within the immune-related QTL regions reported in previous studies. Furthermore, several significant SNPs fell into the regions harboring known immunity-related genes, 14 of them fell into the regions which harbor some known T cell-related genes. Conclusions Our study demonstrated that genome-wide association studies would be a feasible way for revealing the potential genetics variants affecting T-cell subpopulations. Results herein lay a preliminary foundation for further identifying the causal mutations underlying swine immune capacity in follow-up studies.

Background Copy Number Variations (CNVs) have been shown important in both normal phenotypic variability and disease susceptibility, and are increasingly accepted as another important source of genetic variation complementary to single nucleotide polymorphism (SNP). Comprehensive identification and cataloging of pig CNVs would be of benefit to the functional analyses of genome variation. Results In this study, we performed a genome-wide CNV detection based on the Porcine SNP60 genotyping data of 474 pigs from three pure breed populations (Yorkshire, Landrace and Songliao Black) and one Duroc × Erhualian crossbred population. A total of 382 CNV regions (CNVRs) across genome were identified, which cover 95.76Mb of the pig genome and correspond to 4.23% of the autosomal genome sequence. The length of these CNVRs ranged from 5.03 to 2,702.7kb with an average of 250.7kb, and the frequencies of them varied from 0.42 to 20.87%. These CNVRs contains 1468 annotated genes, which possess a great variety of molecular functions, making them a promising resource for exploring the genetic basis of phenotypic variation within and among breeds. To confirmation of these findings, 18 CNVRs representing different predicted status and frequencies were chosen for validation via quantitative real time PCR (qPCR). Accordingly, 12 (66.67%) of them was successfully confirmed. Conclusions Our results demonstrated that currently available Porcine SNP60 BeadChip can be used to capture CNVs efficiently. Our study firstly provides a comprehensive map of copy number variation in the pig genome, which would be of help for understanding the pig genome and provide preliminary foundation for investigating the association between various phenotypes and CNVs.

Background The mixed model based single locus regression analysis (MMRA) method was used to analyse the common simulated dataset of the 15th QTL-MAS workshop to detect potential significant association between single nucleotide polymorphisms (SNPs) and the simulated trait. A Wald chi-squared statistic with df =1 was employed as test statistic and the permutation test was performed. For adjusting multiple testing, phenotypic observations were permutated 10,000 times against the genotype and pedigree data to obtain the threshold for declaring genome-wide significant SNPs. Linkage disequilibrium (LD) in term of D' between significant SNPs was quantified and LD blocks were defined to indicate quantitative trait loci (QTL) regions. Results The estimated heritability of the simulated trait is approximately 0.30. 82 genome-wide significant SNPs (P < 0.05) on chromosomes 1, 2 and 3 were detected. Through the LD blocks of the significant SNPs, we confirmed 5 and 1 QTL regions on chromosomes 1 and 3, respectively. No block was detected on chromosome 2, and no significant SNP was detected on chromosomes 4 and 5. Conclusion MMRA is a suitable method for detecting additive QTL and a fast method with feasibility of performing permutation test. Using LD blocks can effectively detect QTL regions.

Background Genomic breeding value estimation is the key step in genomic selection. Among many approaches, BLUP methods and Bayesian methods are most commonly used for estimating genomic breeding values. Here, we applied two BLUP methods, TABLUP and GBLUP, and three Bayesian methods, BayesA, BayesB and BayesCπ, to the common dataset provided by the 15th QTL-MAS Workshop to evaluate and compare their predictive performances. Results For the 1000 progenies without phenotypic values, the correlations between GEBVs by different methods ranged from 0.812 (GBLUP and BayesCπ) to 0.997 (TABLUP and BayesB). The accuracies of GEBVs (measured as correlations between true breeding values (TBVs) and GEBVs) were from 0.774 (GBLUP) to 0.938 (BayesCπ) and the biases of GEBVs (measure as regressions of TBVs on GEBVs) were from 1.033 (TABLUP) to 1.648 (GBLUP). The three Bayesian methods and TABLUP had similar accuracy and bias. Conclusions BayesA, BayesB, BayesCπ and TABLUP performed similarly and satisfactorily and remarkably outperformed GBLUP for genomic breeding value estimation in this dataset. TABLUP is a promising method for genomic breeding value estimation because of its easy computation of reliabilities of GEBVs and its easy extension to real life conditions such as multiple traits and consideration of individuals without genotypes.

Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.

Enterotoxigenic Escherichia coli (ETEC) expressing F4 fimbria is the major pathogenic bacteria causing diarrhoea in neonatal and post-weaning piglets. Previous studies have revealed that the susceptibility to ETEC F4ab/F4ac is an autosomal Mendelian dominant trait and the loci controlling the F4ab/F4ac receptor are located on SSC13q41, between markers SW207 and S0283. To pinpoint these loci and further validate previous findings, we performed a genome-wide association study (GWAS) using a two generation family-based population, consisting of 301 piglets with phenotypes of susceptibility to ETEC F4ab/F4ac by the vitro adhesion test. The DNA of all piglets and their parents was genotyped using the Illumina PorcineSNP60 BeadChip, and 50,972 and 50,483 SNPs were available for F4ab and F4ac susceptibility, respectively, in the association analysis after quality control. In summary, 28 and 18 significant SNPs (p

Background Most recently, with maturing of bovine genome sequencing and high throughput SNP genotyping technologies, a large number of significant SNPs associated with economic important traits can be identified by genome-wide association studies (GWAS). To further determine true association findings in GWAS, the common strategy is to sift out most promising SNPs for follow-up replication studies. Hence it is crucial to explore the functional significance of the candidate SNPs in order to screen and select the potential functional ones. To systematically prioritize these statistically significant SNPs and facilitate follow-up replication studies, we developed a bovine SNP annotation tool (Snat) based on a web interface. Results With Snat, various sources of genomic information are integrated and retrieved from several leading online databases, including SNP information from dbSNP, gene information from Entrez Gene, protein features from UniProt, linkage information from AnimalQTLdb, conserved elements from UCSC Genome Browser Database and gene functions from Gene Ontology (GO), KEGG PATHWAY and Online Mendelian Inheritance in Animals (OMIA). Snat provides two different applications, including a CGI-based web utility and a command-line version, to access the integrated database, target any single nucleotide loci of interest and perform multi-level functional annotations. For further validation of the practical significance of our study, SNPs involved in two commercial bovine SNP chips, i.e., the Affymetrix Bovine 10K chip array and the Illumina 50K chip array, have been annotated by Snat, and the corresponding outputs can be directly downloaded from Snat website. Furthermore, a real dataset involving 20 identified SNPs associated with milk yield in our recent GWAS was employed to demonstrate the practical significance of Snat. Conclusions To our best knowledge, Snat is one of first tools focusing on SNP annotation for livestock. Snat confers researchers with a convenient and powerful platform to aid functional analyses and accurate evaluation on genes/variants related to SNPs, and facilitates follow-up replication studies in the post-GWAS era.

Recently, the Bayesian least absolute shrinkage and selection operator (LASSO) has been successfully applied to multiple quantitative trait loci (QTL) mapping, which assigns the double-exponential prior and the Student's t prior to QTL effect that lead to the shrinkage estimate of QTL effect. However, as reported by many researchers, the Bayesian LASSO usually cannot effectively shrink the effects of zero-effect QTL very close to zero. In this study, the double-exponential prior and Student's t prior are modified so that the estimate of the effect for zero-effect QTL can be effectively shrunk toward zero. It is also found that the Student's t prior is virtually the same as the Jeffreys' prior, since both the shape and scale parameters of the scaled inverse Chi-square prior involved in the Student's t prior are estimated very close to zero. Besides the two modified Bayesian Markov chain Monte Carlo (MCMC) algorithms, an expectation-maximization (EM) algorithm with use of the modified double-exponential prior is also adapted. The results shows that the three new methods perform similarly on true positive rate and false positive rate for QTL detection, and all of them outperform the Bayesian LASSO.

Major histocompatibility complex (MHC) class II molecules play an important role in immunology by presenting antigens to T lymphocytes. As a key member of the MHC class II gene family, the DQB1 gene is involved in interacting with T cells in immune reactions. This study was designed to screen variations in exon 3 of the DQB1 gene in Large White, Landrace and Songliao Black pigs, and to investigate the association between DQB1 gene polymorphisms and peripheral blood T lymphocyte subsets in Large White samples. In addition, the spatial transcription profile of the DQB1 gene was examined, and the effect of gene polymorphisms on its mRNA level was further analyzed. One missense mutation in exon 3 of the DQB1 gene was first identified by DNA pool sequencing, and samples were genotyped by using the polymerase chain reactionrestriction fragment length polymorphism method. Statistical analysis indicated that the DQB1 genotype was significantly associated with CD4 ＋ CD8 － , CD4 － CD8 ＋ and CD4 ＋ /CD8 ＋ indexes (P 0.05). These results suggest that the DQB1 gene may be a promising candidate gene for porcine disease resistance.

The multiple-SNP analysis has been studied by many researchers, in which the effects of multiple SNPs are simultaneously estimated and tested in a multiple linear regression. The multiple-SNP association analysis usually has higher power and lower false-positive rate for detecting causative SNP(s) than single marker analysis (SMA). Several methods have been proposed to simultaneously estimate and test multiple SNP effects. In this research, a fast method called MEML (Mixed model based Expectation-Maximization Lasso algorithm) was developed for simultaneously estimate of multiple SNP effects. An improved Lasso prior was assigned to SNP effects which were estimated by searching the maximum joint posterior mode. The residual polygenic effect was included in the model to absorb many tiny SNP effects, which is treated as missing data in our EM algorithm. A series of simulation experiments were conducted to validate the proposed method, and the results showed that compared with SMMA, the new method can dramatically decrease the false-positive rate. The new method was also applied to the 50k SNP-panel dataset for genome-wide association study of milk production traits in Chinese Holstein cattle. Totally, 39 significant SNPs and their nearby 25 genes were found. The number of significant SNPs is remarkably fewer than that by SMMA which found 105 significant SNPs. Among 39 significant SNPs, 8 were also found by SMMA and several well-known QTLs or genes were confirmed again; furthermore, we also got some positional candidate gene with potential function of effecting milk production traits. These novel findings in our research should be valuable for further investigation.

Background Improvements in poultry production within the past 50 years have led to increased muscle yield and growth rate, which may be contributing to an increased rate and development of new muscle disorders in chickens. Previously reported muscle disorders and conditions are generally associated with poor meat quality traits and have a significant negative economic impact on the poultry industry. Recently, a novel myopathy phenotype has emerged which is characterized by palpably “hard” or tough breast muscle. The objective of this study is to identify the underlying biological mechanisms that contribute to this emerging muscle disorder colloquially referred to as “Wooden Breast”, through the use of RNA-sequencing technology. Methods We constructed cDNA libraries from five affected and six unaffected breast muscle samples from a line of commercial broiler chickens. After paired-end sequencing of samples using the Illumina Hiseq platform, we used Tophat to align the resulting sequence reads to the chicken reference genome and then used Cufflinks to find significant changes in gene transcript expression between each group. By comparing our gene list to previously published histology findings on this disorder and using Ingenuity Pathways Analysis (IPA®), we aim to develop a characteristic gene expression profile for this novel disorder through analyzing genes, gene families, and predicted biological pathways. Results Over 1500 genes were differentially expressed between affected and unaffected birds. There was an average of approximately 98 million reads per sample, across all samples. Results from the IPA analysis suggested “Diseases and Disorders” such as connective tissue disorders, “Molecular and Cellular Functions” such as cellular assembly and organization, cellular function and maintenance, and cellular movement, “Physiological System Development and Function” such as tissue development, and embryonic development, and “Top Canonical Pathways” such as, coagulation system, axonal guidance signaling, and acute phase response signaling, are associated with the Wooden Breast disease. Conclusions There is convincing evidence by RNA-seq analysis to support localized hypoxia, oxidative stress, increased intracellular calcium, as well as the possible presence of muscle fiber-type switching, as key features of Wooden Breast Disease, which are supported by reported microscopic lesions of the disease. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1623-0) contains supplementary material, which is available to authorized users.